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Gcp gemini api developer

Skill Raishin/vanguard-frontier-agentic/skills/gcp/gcp-gemini-api-developer

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npx -y skills add Raishin/vanguard-frontier-agentic --skill gcp-gemini-api-developer

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Build, integrate, and debug Gemini API applications on Google Cloud Agent Platform (formerly Vertex AI) using the unified google-genai SDK. Covers text generation, multimodal inputs, function calling, structured output, embeddings, context caching, batch prediction, streaming, Live API (bidirectional voice/video), and model tuning across Python, TypeScript/JavaScript, Go, Java, and C#. Use when building Gemini-powered applications, migrating from deprecated Vertex AI or google-generativeai SDKs, or integrating Gemini capabilities into a GCP-hosted service.

SKILL.md

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GCP Gemini API Developer

IMPORTANT BRANDING NOTE

"Agent Platform" (full name: Gemini Enterprise Agent Platform) was previously named "Vertex AI". Many web resources still use "Vertex AI" branding.

CRITICAL SDK NOTE

The following SDKs are DEPRECATED and must NOT be used:

google-cloud-aiplatform (Python)
@google-cloud/vertexai (Node.js)
google-generativeai (Python — Gemini Developer API SDK)

Use ONLY the unified Gen AI SDK:

Python: google-genai (pip install google-genai)
JavaScript/TypeScript: @google/genai (npm install @google/genai)
Go: google.golang.org/genai (go get google.golang.org/genai)
Java: com.google.genai:google-genai
C#/.NET: Google.GenAI (dotnet add package Google.GenAI)

Authentication

# Application Default Credentials (ADC) — preferred
export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'  # Use 'global' for automatic region routing
export GOOGLE_GENAI_USE_VERTEXAI=true

Model Selection

gemini-3.1-pro-preview — complex reasoning, coding, research (1M tokens)
gemini-3-flash-preview — fast, balanced, multimodal (1M tokens)
gemini-3.1-flash-lite-preview — high-frequency lightweight tasks
gemini-3-pro-image-preview — image generation and editing
gemini-live-2.5-flash-native-audio — Live Realtime API (bidirectional audio/video)
DEPRECATED (do not use): gemini-2.0-, gemini-1.5-, gemini-1.0-*, gemini-pro

Quick Start (Python)

from google import genai
client = genai.Client  # picks up env vars automatically
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="Explain transformer architecture"
)
print(response.text)

Reference Directory

Load only when needed:

Scenario	Trigger Keywords	Reference
Text + multimodal	chat, image, video, audio, streaming	references/text-multimodal.md
Function calling + tools	tool use, function call, grounding, code execution	references/tools.md
Structured output	JSON, schema, structured, typed response	references/structured-output.md
Embeddings	embedding, semantic search, vector	references/embeddings.md
Context caching	cache, large context, caching tokens	references/caching.md
Batch prediction	batch, async, large dataset	references/batch.md
Live API	live, realtime, voice, video streaming, bidirectional	references/live-api.md
Model tuning	fine-tune, SFT, preference tuning	references/tuning.md
Safety	safety filter, threshold, harm category	references/safety.md
SDK migration	migrate, deprecated, upgrade	references/migration.md

Core Rules

ALWAYS use the unified google-genai SDK family. If user code imports google-cloud-aiplatform, @google-cloud/vertexai, or google-generativeai, flag it as deprecated and provide migration guidance.
Use location="global" (global endpoint) by default for automatic capacity routing. Only use a specific region if the user explicitly requests it.
Initialize the client without parameters when environment variables are set — don't hardcode project/location in code.
gemini-3.1-pro-preview ≠ gemini-3-pro-preview — the latter does NOT exist; use the correct model IDs.
Context caching (CachedContent) reduces cost for repeated large contexts (system prompts, documents) — recommend it proactively for production workloads with stable large contexts.
For production, consult docs for stable model version aliases rather than using -preview models.
Batch prediction (BatchJob) is for async large-dataset inference — use it instead of looping generate_content for bulk processing.

Official Docs

Security Notes

Read-only advisory. Never embed API keys or service account credentials in code examples. Use ADC and environment variables. Do not call batch jobs or fine-tuning jobs on production data without explicit user approval.

What ships with it: 1 file

1.1 KB alongside SKILL.md

metadata.json1.1 KB

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